4th Line Perreault: Still Underappreciated

Author’s note: the majority of this article was written prior to Sunday, December 3rd, and thus does not reflect Perreault’s recent move back into the top 6 as Kyle Connor is out with injury. Numbers and charts shown will reflect stats up to (but not including) game #27 vs. the Ottawa Senators.

As of the morning of Saturday, December 2nd, 2017, the Winnipeg Jets are atop the Central Division and Western Conference and would be tied with the Tampa Bay Lightning for first in the entire NHL if the Atlantic foes were not one game behind. While it is only 26 games into the season, fans are feeling mighty good that everything is finally seemingly coming together, though the players are not ready to settle just yet with 56 more games to go.

Though it is still seven months until June, we should still delve into the why and how in figuring out why the Jets have looked incredibly strong as of late. After struggling out of the gate with Steve Mason (who was regaining his own momentum before having to go on the Injured Reserve with a concussion), Connor Hellebuyck has been better than average and significantly better than his last season. The lack of injuries (and holdouts) and the addition of Dmitry Kulikov have shored up many defensive problems that have plagued the Jets in the past. Don’t look now, but the results of the power play and penalty kill have improved as well (though problems still need to be very obviously addressed).

And while some may point to the addition of Matt Hendricks as one of the significant reasons for the Jets’ recent success, I am going to point to his teammate that absolutely no one expected to be on his wing: 4th-Line Jesus, Mathieu Perreault.

The Resurrection of the 4th Line

Using Hendricks as the base, here are the (adjusted) stats of the recent 4th lines of the Winnipeg Jets, ordered by CF%. It should not be that shocking to see Perreault’s insertion into the absolute bottom 3 of the forward lineup has that line performing the best of all Hendricks-oriented lines. Not only have they not allowed any goals at 5v5, they’ve scored five of their own while also heavily controlling shots and expected goals. In fact, one could easily argue that they have scored way more than expected, and actually should be expected to give up a healthy dose of goals themselves with the amount they are scoring (on a lesser rate of shots against). But certainly, this iteration of the 4th line has generated more shots and goals in a shorter time span than most other previous Winnipeg Jets 4th lines – and most of this can certainly be attributed to #85.

With Perreault being on the 4th line, it has had an unintended side effect that has long plagued the fans and been a significant reason for the dislike of Paul Maurice: with the results they are getting (and luckily early on), they are getting trust and ice-time at 5v5. Just look at the bottom 3 lines and do some quick math in your head: 4.95 in one game played; (28.3 TOI / 7 GP) 4.04 TOI per game, and; (25.42 TOI / 5 GP) 5.08 TOI per game. The combination of Armia-Hendricks-Perreault is getting 6.13 minutes at 5v5 per game, which theoretically gives the top lines more rest. These minutes add up quickly in an 82-game season, and should pay dividends should the Jets reach the playoffs, with the Jets’ top players not expending as much energy early on and less at risk for significant or cumulative injury.

Perreault’s Return and the Effect on the Team

What was noticeably worrisome early on in the Jets beginning games was their share of shots, and how little of them they were generating. Though in many games they still scored and many began to proclaim that Corsi was dead for the hundredth time (and still wrong), I hope JetsNation’s fans are familiar with the predictive ability of that metric in comparison to GF%. Many saw the lack of shot generation as ill-fitting with an offensive-laden team such as the Jets, who (while still incredibly talented) are unlikely to carry what is already a very high shooting percentage towards the end of the season.

What I have highlighted in the red box is the games played since Perreault has returned. Before his return, the rate of shots were finally starting to creep up to match the rate of shots given up for the second or third time, but now have skyrocketed. As we can see yet again, the 4th line with the return of Perreault is a big reason for this, generating close to a shot per minute of ice time (and allowing half a shot as well!):

And of course, let’s not forget the kind of shots that Perreault is generating on the ice in comparison to the team (without Little and Scheifele on the ice). They are coming from right in front of the opponent’s net, either by getting behind defenders or going to the hard areas and getting rebounds. What coaches love 4th lines to do is cause havoc in the opposition’s end through hits and forechecking; but with Perreault, the advantage becomes getting the shots from those high danger areas as well:

And for good measure: Hendricks and Perreault in their small amount of time together are also really good at not allowing shots in front of their own goaltender – except for one tiny area in the slot:

But Seriously: Perreault Makes Everyone Better

Perreault has always been loved by the fans for his seemingly unending hustle on the ice; but he has also been an analytics darling seemingly forever due to his unique quality of making those he plays with better in the underlying metrics. This can easily be seen this year through looking at Micah Blake McCurdy’s visuals of Joel Armia and Matt Hendricks, with Perreault’s impact as a linemate highlighted by previous JetsNation writer, Garret Hohl:

With the Perreault playing with Armia and Hendricks, both of their shot rates spike to considerably above the league average of ~58, and their line’s shooting percentage (or goal percentage of each shot taken) also skyrockets. Obviously, when Perreault is feeding you assists from behind the And of course, if you need to look at the actual numbers for Armia and Hendricks with and without Perreault, here they are:

WOWY Data from NaturalStatTrick.com (5v5 Score, Zone, Venue Adjusted)

The column to focus on is the “CF% Without Perreault” – i.e., the player’s CF% without Perreault on the ice (in the TOI Away from him). With the exception of Hendricks’ somewhat ridiculous 79.09% CF% without Perreault (albeit in 22 minutes of 5v5 ice time), every player – especially Armia – suffers a drop in their CF% when not with Mathieu Perreault. And of course, this is not limited to the 2017-18 season:

What you see in the above chart is all of Perreault’s linemates for the previous three seasons in which he was on the ice with the player for at least 100 minutes of 5v5. The colour coding is as such: with three possible scenarios for the CF%, green is the best, yellow is the middle, and red is the worst. What should be unsurprising by now is that with the exception of Blake Wheeler, almost all players have a lower share of controlling shots without Perreault (a number of them dropping below 50% as well). While these sorts of WOWYs should always be taken with somewhat of a grain a salt due to time-on-ice and ignoring the other linemate (and defence pairings) in addition to not taking the opposition into account, it is hard to ignore what happens when Perreault is on your wing.

Perreault has always been one of the most underappreciated players in the NHL. Even now when he is loved by the Jets, their management, and their fans, I still think Mathieu Perreault is underappreciated. He is one of the most dynamic players who has the special ability to elevate the level of play of his four teammates and can help create matchup problems no matter where is he in the lineup. Praying that Perreault continues to stay healthy and not attract vicious hits and slashes to various parts of his body, simply having him on the ice is helping the Jets push towards not just a playoff spot, but even being the top team in the league.

Agreed. Perreault on the so-called-fourth line (third IMHO) makes matchups so hard for opposing teams. Hopefully by the reduced ice time he stays healthy longer as well. Perreault is such a pro at playing where the team needs him. Class act.

I find it interesting that the conclusion of the article linked to in order to justify the predictive value of Corsi, basically says that xG is a better metric than Corsi. And yet our dear author plows ahead using Corsi for his analysis while ignoring xG…

Not that I disagree with him, or think that Corsi is dead, or is not valuable, or so on, but just that Corsi only captures a fairly limited amount of information – it’s basically just shots – and so in this day of machine learning and advanced stats, we can do better. Clearly Corsi has not done very well in predicting the Jets’ current success, and while there are sure to be outliers, we should always be asking ourselves what metric can we use to improve our predictive abilities. And we have one: it’s xG. So why are we still using Corsi?

Don’t think it went unnoticed, good reader! With the article I linked, the xG model used is Dawson Spriging’s (AKA @DTMAboutHeart), whom is now employed by the ownership group of the Colorado Avalanche – unfortunately meaning there is no 2017-18 xG of his kind. While there is Corsica’s, there is some question of it’s predictability in comparison to Corsi (I believe Manny himself has admitted it’s still in progress (though I still like using it)), and generally accepted as the lesser of the two xG models (DTM vs Corsica). For these reasons, and for the general knowledge out there today about Corsi, I decided to trudge forward with that data which is widely available in many capacities.

Of course, as we all should know, Corsi is better for analysis than perhaps, say, 20 games worth of a sample size of goals scored for one team, wouldn’t you agree? :^)

You make good points, and raise an interesting one. The metrics that go into calculating expected goals are somewhat subjective. Wrist shot vs slapshot, for instance, or whether or not to include shooter’s skill (I believe Dawson does but Manny does not). It’s not a standardized metric, and if we want to improve it in the future, we end up in an awkward situation where yesterday’s expected goals may not be the same as today’s; do we have to create a new name for it, every time we iterate on it?

If I wasn’t so lazy, I would be inclined to dump a bunch of stats into an analysis program myself and perform PCA or some neural network magic and see what popped out. Let the computer worry about how to weight all these stats together – we just want to maximize our predictive success. I’m a bit surprised no-one else has tried something like that yet.